DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 1 recites the limitation: “… adjusting interactions between the entity and the user based on the contact points; and presenting the adjusted script to an agent …” (emphasis added to accentuate insufficient antecedent basis). However, neither “a script” nor “an adjusted script” is ever introduced earlier in the claim.
For purposes of examination, the limitation is interpreted as:
“… adjusting interactions between the entity and the user based on the contact points; and presenting the interactions to an agent …”.
Claim 8 recites the limitation: “… finding data most relevant to the user associated with the interaction … said agent conducting the interaction … (emphasis added to accentuate insufficient antecedent basis)”. The preamble only introduces the plural “interactions” (“to provide interactions between an entity and a user”). Therefore, the singular “the interaction” lacks proper antecedent basis. Further, in the final limitation, the claim refers to an agent associated with “the quantum computing system”. However, the preamble introduces “a quantum communication system” (emphasis added to accentuate insufficient antecedent basis).
Claim 14:
“the user”: In the analyzing step, the claim recites: “finding data most relevant to the user”. “A user” is not introduced until a few limitations later (“the contact points being associated with a user”).
“the interaction”: In the analyzing step, the claim references “associated with the interaction”. “An interaction” is not formally introduced until the very last word of the claim.
“the entity”: The adjusting step recites: “adjust interactions between the entity and the user”. “An entity” is never introduced anywhere in Claim 14.
“the adjusted script”: Similar to Claim 1, the final limitation recites: “present the adjusted script” but “a script” is never introduced in the claim.
“the converted data”: The contact points limitation recites: “said contact points based on the converted data”. While the claim previously mentions converting an audio file into a text file, “converted data” itself is never explicitly introduced, resulting in an implicit/missing antecedent basis issue.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over Qualtrics (“Behavioral Analytics: How to Use Behavioral Data to Improve the Bottom Line”; Pages: 14; 2023; URL: https://www.qualtrics.com/articles/strategy-research/behavioral-analytics) in view of Hui et al., hereinafter referred to as Hui (“QC — Control Quantum Computing with Unitary Operators, Interference & Entanglement”; Pages: 23; URL: https://jonathan-hui.medium.com/qc-control-quantum-computing-with-unitary-operators-interference-entanglement-7790c69f6e98) in further view of Yang et al., hereinafter referred to as Yang (“Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition”; 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); Pages: 5; 2021; DOI: https://arxiv.org/abs/2010.13309).
As per claim 1, Qualtrics discloses a method for implementing a (Qualtrics: Pg. 6, Section “How does behavioral analytics work?” discloses “every user interaction [claimed provide interactions between an entity and a user] with digital channels [claimed A method for implementing a communication system, said implementing the communication system]”), said method comprising:
collecting, using an artificial intelligence machine learning (“AI/ML”) module, a plurality of data, the plurality of data including (Qualtrics: Pg. 2, Section “What is behavioral analytics?” discloses “uses a combination of artificial intelligence (AI), machine learning, and big data analytics [claimed collecting, using an artificial intelligence machine learning (“AI/ML”) module, a plurality of data, the plurality of data including:]”):
user-relevant historical data (Qualtrics: Pg. 6, Section “How does behavioral analytics work” discloses “historic data points [claimed user-relevant historical data]”); and
current behavioral data (Qualtrics: Pg. 6, Section “How does behavioral analytics work?” discloses “user behavior on a website or product in real time [claimed current behavioral data]”);
analyzing, (Qualtrics: Pgs. 4-5, Section “What is the return on investment (ROI) on behavioral analytics in business?” discloses “By identifying peoples' needs, wants, concerns [claimed said analyzing finding data most relevant to the user], and the type of messages they notice, you can connect with each one personally [claimed analyzing, the collected data]”);
shrinking, (Qualtrics: Pg. 7, Section “Types of behavioral analytics” discloses “breaks it into groups [claimed shrinking, the data into smaller datasets]”);
relevancy-wise (Qualtrics: Pgs. 4-5, Section “What is the return on investment (ROI) on behavioral analytics in business?” discloses “separating the most engaged people from the least [claimed relevancy-wise]”); and
size-wise (Qualtrics: Pg. 7, Section “Types of behavioral analytics” discloses “takes the data from a given dataset and breaks it [claimed size-wise]”);
the shrinking includes (Qualtrics: Pg. 7, Section “Types of behavioral analytics” discloses “breaks it into groups [claimed the shrinking includes to]”):
creating, (Qualtrics: Pg. 4, Section “Why should we care about behavioral analytics?” discloses “Understanding customer touchpoints [claimed creating, a plurality of contact points, the plurality of contact points being associated with the user]”);
adjusting interactions between the entity and the user based on the contact points (Qualtrics: Pgs. 4-5, Section “What is the return on investment (ROI) on behavioral analytics in business?” discloses “you can tailor your marketing messages [claimed adjusting interactions between the entity and the user based on the contact points]”); and
presenting the adjusted script to an agent associated with the entity (Qualtrics: Pg. 9, Section “Customer experience (CX)” discloses “flag words or actions that are off script [claimed adjusted script]” and “agents get valuable time back [claimed presenting the to an agent associated with the entity to conduct an interaction]”).
However, Qualtrics does not explicitly disclose “quantum”, “based on quantum computing”, “using one or more quantum computing processors”, “using a quantum algorithm”, “convert an audio data into a text file”, “retrieve a series of parameters based on the audio data”, and “and the quantum communication system”.
Further, Yang is in the same field of endeavor and teaches “quantum”, “based on quantum computing”, “using one or more quantum computing processors”, “using a quantum algorithm”, “convert an audio data into a text file”, “retrieve a series of parameters based on the audio data”, and “the quantum communication system” (Yang: Pg. 1, Section “1. INTRODUCTION” discloses “quantum machine learning (QML) [claimed quantum] becomes an ideal building block for VFL owing to its advantages on parameter encryption ... based on qubits [claimed based on quantum computing]”; Yang: Pg. 1, Section “ABSTRACT” discloses “quantum computing server [claimed using one or more quantum computing processors]”; Yang: Pg. 1, Section “1. INTRODUCTION” discloses “quantum algorithm [claimed using a quantum algorithm]”; Yang: Pg. 3, Section “4.2. Encoded Acoustic Features from Quantum Device” discloses “audio transcription [claimed convert an audio data into a text file]”; Yang: Pg. 2, Section “3.1. Speech Processing under Vertical Federated Learning” discloses “Mel spectrogram feature vectors are extracted [claimed retrieve a series of parameters based on the audio data]”; Yang: Pg. 1, Section “1. INTRODUCTION” discloses “decentralized quantum-based ASR system [claimed the quantum communication system]”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Qualtrics and Yang before him or her, to modify the communication system of Qualtrics to include the quantum computing processors, quantum algorithms, audio conversion, and parameter retrieval feature as described in Yang. The motivation for doing so would have been to improve the security, feature extraction capabilities, and computational efficiency of the acoustic data processing pipeline.
However, Qualtrics-Yang do not explicitly disclose “ … a single one of the plurality of points corresponding to an up-spin, a single one of the plurality of points corresponding to a down-spin and at least one of the plurality of points corresponding to a superposition of the up-spin and the down-spin …”.
Furthermore, Hui is in the same field of endeavor and teaches a single one of the plurality of points corresponding to an up-spin, a single one of the plurality of points corresponding to a down-spin and at least one of the plurality of points corresponding to a superposition of the up-spin and the down-spin (Hui: Pg. 8, Section “Unitary” discloses “an up-spin or a down-spin state [claimed a single one of the plurality of points corresponding to an up-spin, a single one of the plurality of points corresponding to a down-spin]” and “change the superpositions to [claimed at least one of the plurality of points corresponding to a superposition of the up-spin and the down-spin]”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Qualtrics-Yang, and Hui before him or her, to modify the quantum contact points of Qualtrics-Yang to include the up-spin, down-spin, and superposition states as described in Hui. The motivation for doing so would have been to improve processing efficiency by leveraging the fundamental mechanics of qubits to represent the varying states of the encoded touchpoints, thereby enabling robust representations for quantum processing algorithms.
As per claim 2, Qualtrics-Yang-Hui disclose the method of claim 1 wherein the plurality of data is collected from multiple sources (Qualtrics: Pg. 6, Section “How does behavioral analytics work?” discloses “collected through a behavioral analytics platform, from every user interaction with digital channels [claimed plurality of data is collected from multiple sources]”),
the multiple sources including:
private sources (Qualtrics: Pg. 6, Section “How does behavioral analytics work?” discloses “previous transactional records [claimed private sources]”); and
public sources (Qualtrics: Pg. 10, Section “Employee experience (EX)” discloses “external forums, social media interactions, and review sites [claimed public sources]”).
As per claim 3, Qualtrics-Yang-Hui disclose the method of claim 1 wherein the user-relevant historical data includes a plurality of previous interactions (Qualtrics: Pgs. 2-3, Section “What is behavioral analytics?” discloses “past and present activity [claimed the user-relevant historical data includes a plurality of previous interactions]”).
As per claim 4, Qualtrics-Yang-Hui disclose the method of claim 1 wherein the current behavioral data includes current interactions with the user (Qualtrics: Pg. 6, Section “How does behavioral analytics work?” discloses “user behavior on a website or product in real time [claimed the current behavioral data includes current interactions with the user]”).
As per claim 5, Qualtrics-Yang-Hui disclose the method of claim 1 wherein shrinking the data relevancy-wise includes shrinking the data into data applicable to a specific user (Qualtrics: Pgs. 4-5, Section “What is the return on investment (ROI) on behavioral analytics in business?” discloses “connect with each one personally [claimed shrinking the data into data applicable to a specific user]”).
As per claim 6, Qualtrics-Yang-Hui disclose the method of claim 1 wherein the contact points include: a plurality of hobbies associated with the user (Qualtrics: Pg. 3, Section “When and where do we see behavioral analytics in business?” discloses “analyzing a customer's choices and tastes [claimed a plurality of hobbies associated with the user]”); a time preferable to conduct a call (Qualtrics: Pg. 10, Section “Employee experience (EX)” discloses “understand how and when work gets done by linking it to productivity systems such as calendars [claimed a time preferable to conduct a call]”); and a location associated with the user (Qualtrics: Pgs. 2-3, Section “What is behavioral analytics?” discloses “geographic and demographic data [claimed a location associated with the user]”).
As per claim 7, Qualtrics-Yang-Hui disclose the method of claim 1 wherein the one or more quantum computing processors create one or more quantum algorithms (Yang: Pg. 3, Section “3.3. Random Quantum Circuit” discloses “randomly generated per QCNN model [claimed the one or more quantum computing processors create one or more quantum algorithms]”).
As per claim 8, Qualtrics discloses an apparatus for implementing a (Qualtrics: Pg. 6, Section “How does behavioral analytics work?” discloses “every user interaction [claimed provide interactions between an entity and a user] with digital channels [claimed Apparatus for implementing a communication system, said implementing a communication system]”), said apparatus comprising:
user-relevant historical data (Qualtrics: Pg. 6, Section “How does behavioral analytics work” discloses “historic data points [claimed user-relevant historical data]”); current behavioral data (Qualtrics: Pg. 6, Section “How does behavioral analytics work?” discloses “user behavior on a website or product in real time [claimed current behavioral data]”);
an artificial intelligence machine learning (“AI/ML”) module, the AI/ML module operable to collect the user-relevant historical data and current behavioral data (Qualtrics: Pg. 2, Section “What is behavioral analytics?” discloses “uses a combination of artificial intelligence (AI), machine learning [claimed an artificial intelligence machine learning (“AI/ML”) module, the AI/ML module operable to collect the user-relevant historical data and current behavioral data]”);
one or more (Qualtrics: Pg. 6, Section “How does behavioral analytics work?” discloses “platform [claimed one or more computing processors, the one or more computing processors operable to]”):
analyze the user-relevant historical data and the current behavioral data, said analyze finding data most relevant to the user associated with the interaction (Qualtrics: Pgs. 4-5, Section “What is the return on investment (ROI) on behavioral analytics in business?” discloses “By identifying peoples' needs, wants, concerns [claimed said analyze finding data most relevant to the user associated with the interaction], and the type of messages they notice, you can connect with each one personally [claimed analyze the user-relevant historical data and the current behavioral data]”); and
shrink the user-relevant historical data and the current behavioral data into smaller datasets, said shrinking relevancy-wise and size-wise (Qualtrics: Pg. 7, Section “Types of behavioral analytics” discloses “breaks it into groups [claimed shrink the user-relevant historical data and the current behavioral data into smaller datasets, said shrinking relevancy-wise and size-wise]”);
the shrinking comprising using a plurality of (Qualtrics: Pg. 7, Section “Types of behavioral analytics” discloses “takes the data from a given dataset and breaks it [claimed the shrinking comprising using a plurality of algorithms to]”):
a plurality of contact points, the plurality of contact points being created using the one or more (Qualtrics: Pg. 4, Section “Why should we care about behavioral analytics?” discloses “Understanding customer touchpoints [claimed a plurality of contact points, the plurality of contact points being created using the one or more computing processors, the plurality of contact points being associated with the user]”);
adjusting interactions between the entity and the user based on the contact points (Qualtrics: Pgs. 4-5, Section “What is the return on investment (ROI) on behavioral analytics in business?” discloses “you can tailor your marketing messages [claimed adjusting interactions between the entity and the user based on the contact points]”); and
presenting the interactions to an agent associated with the entity (Qualtrics: Pg. 5, Section “Customer experience (CX)” discloses “customers get the help they expect and agents get valuable time back [claimed presenting the interactions to an agent associated with the entity, said agent conducting the interaction]”).
However, Qualtrics does not explicitly disclose “quantum”, “based on quantum computing”, “using one or more quantum computing processors”, “using a plurality of quantum algorithms”, “convert an audio file into a text file”, “retrieve a series of parameters based on the audio file”, and “and the quantum computing system”.
Further, Yang is in the same field of endeavor and teaches “quantum”, “based on quantum computing”, “using one or more quantum computing processors”, “using a plurality of quantum algorithms”, “convert an audio file into a text file”, “retrieve a series of parameters based on the audio file”, and “and the quantum computing system” (Yang: Pg. 1, Section “1. INTRODUCTION” discloses “quantum machine learning (QML) [claimed quantum] becomes an ideal building block for VFL owing to its advantages on parameter encryption ... based on qubits [claimed based on quantum computing]”; Yang: Pg. 1, Section “ABSTRACT” discloses “quantum computing server [claimed using one or more quantum computing processors]”; Yang: Pg. 1, Section “1. INTRODUCTION” discloses “quantum algorithm [claimed using a plurality of quantum algorithms]”; Yang: Pg. 3, Section “4.2. Encoded Acoustic Features from Quantum Device” discloses “audio transcription [claimed convert an audio file into a text file]”; Yang: Pg. 2, Section “3.1. Speech Processing under Vertical Federated Learning” discloses “Mel spectrogram feature vectors are extracted [claimed retrieve a series of parameters based on the audio file]”; Yang: Pg. 1, Section “1. INTRODUCTION” discloses “decentralized quantum-based ASR system [claimed and the quantum computing system]”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Qualtrics and Yang before him or her, to modify the communication system of Qualtrics to include the quantum computing processors, quantum algorithms, audio conversion, and parameter retrieval feature as described in Yang. The motivation for doing so would have been to improve the security, feature extraction capabilities, and computational efficiency of the acoustic data processing pipeline (Yang: Pg. 1, Section “1. INTRODUCTION”).
However, Qualtrics-Yang do not explicitly disclose “a single one of the plurality of points corresponding to an up-spin, a single one of the plurality of points corresponding to a down-spin and at least one of the plurality of points corresponding to a superposition of the up-spin and the down-spin”.
Furthermore, Hui is in the same field of endeavor and teaches “a single one of the plurality of points corresponding to an up-spin, a single one of the plurality of points corresponding to a down-spin and at least one of the plurality of points corresponding to a superposition of the up-spin and the down-spin” (Hui: Pg. 8, Section “Unitary” discloses “an up-spin or a down-spin state [claimed a single one of the plurality of points corresponding to an up-spin, a single one of the plurality of points corresponding to a down-spin]” and “change the superpositions to [claimed at least one of the plurality of points corresponding to a superposition of the up-spin and the down-spin]”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Qualtrics-Yang, and Hui before him or her, to modify the quantum contact points of Qualtrics-Yang to include the up-spin, down-spin, and superposition states feature as described in Hui. The motivation for doing so would have been to improve processing efficiency by leveraging the fundamental mechanics of qubits to represent the varying states of the encoded touchpoints, thereby enabling robust representations for quantum processing algorithms.
As per claim 9, Qualtrics-Yang-Hui disclose the apparatus of claim 8 wherein the plurality of data is collected from multiple sources (Qualtrics: Pg. 6, Section “How does behavioral analytics work?” discloses “collected through a behavioral analytics platform, from every user interaction with digital channels [claimed Qualtrics-Yang-Hui disclose the apparatus of claim 8 wherein the plurality of data is collected from multiple sources]”),
the multiple sources including:
private sources (Qualtrics: Pg. 6, Section “How does behavioral analytics work?” discloses “previous transactional records [claimed private sources]”); and
public sources (Qualtrics: Pg. 10, Section “Employee experience (EX)” discloses “external forums, social media interactions, and review sites [claimed public sources]”).
As per claim 10, Qualtrics-Yang-Hui disclose the apparatus of claim 8 wherein the user-relevant historical data includes a plurality of previous interactions (Qualtrics: Pgs. 2-3, Section “What is behavioral analytics?” discloses “past and present activity [claimed the user-relevant historical data includes a plurality of previous interactions]”).
As per claim 11, Qualtrics-Yang-Hui disclose the apparatus of claim 8 wherein the current behavioral data includes current interactions with the user (Qualtrics: Pg. 6, Section “How does behavioral analytics work?” discloses “user behavior on a website or product in real time [claimed the current behavioral data includes current interactions with the user]”).
As per claim 12, Qualtrics-Yang-Hui disclose the apparatus of claim 8 wherein shrinking the data relevancy-wise includes shrinking into datasets applicable to a single user (Qualtrics: Pgs. 4-5, Section “What is the return on investment (ROI) on behavioral analytics in business?” discloses “connect with each one personally [claimed shrinking into datasets applicable to a single user]”).
As per claim 13, Qualtrics-Yang-Hui disclose the apparatus of claim 8 wherein the contact points include:
a plurality of hobbies associated with the user (Qualtrics: Pg. 3, Section “When and where do we see behavioral analytics in business?” discloses “analyzing a customer's choices and tastes [claimed a plurality of hobbies associated with the user]”);
a time preferable to conduct the interaction (Qualtrics: Pg. 10, Section “Employee experience (EX)” discloses “understand how and when work gets done by linking it to productivity systems such as calendars [claimed a time preferable to conduct the interaction]”); and
a location associated with the user (Qualtrics: Pgs. 2-3, Section “What is behavioral analytics?” discloses “geographic and demographic data [claimed a location associated with the user]”).
As per claim 14, Qualtrics discloses a (Qualtrics: Pg. 6, Section “How does behavioral analytics work?” discloses “every user interaction with digital channels [claimed A communication system, the communication system comprising:]”):
a collection of data, said data including user-relevant historical data and current behavioral data, the collection of data being performed using one or more (Qualtrics: Pgs. 2-3, Section “What is behavioral analytics?” discloses “past and present activity - their user behavior data [claimed a collection of data, said data including user-relevant historical data and current behavioral data]” and Pg. 3 “platform [claimed the collection of data being performed using one or more computing processors]”);
the one or more (Qualtrics: Pg. 6, Section “How does behavioral analytics work?” discloses “platform [claimed the one or more computing processors further operable to]”):
analyze the collected data, said analyzing finding data most relevant to the user associated with the interaction (Qualtrics: Pgs. 4-5, Section “What is the return on investment (ROI) on behavioral analytics in business?” discloses “By identifying peoples' needs, wants, concerns [claimed said analyzing finding data most relevant to the user associated with the interaction], and the type of messages they notice, you can connect with each one personally [claimed analyze the collected data]”); and
shrink the data into smaller datasets, said shrinking relevancy-wise and size-wise (Qualtrics: Pg. 7, Section “Types of behavioral analytics” discloses “breaks it into groups [claimed shrink the data into smaller datasets, said shrinking relevancy-wise and size-wise]”),
wherein the shrinking comprises using one or more (Qualtrics: Pg. 7, Section “Types of behavioral analytics” discloses “takes the data from a given dataset and breaks it [claimed wherein the shrinking comprises using one or more algorithms to]”):
create a plurality of (Qualtrics: Pg. 2, Section “What is behavioral analytics?” discloses “uses a combination of artificial intelligence (AI), machine learning [claimed create a plurality of algorithms, the plurality of algorithms]”);
create a plurality of contact points, said contact points based on the converted data, the contact points being associated with a user, (Qualtrics: Pg. 4, Section “Why should we care about behavioral analytics?” discloses “Understanding customer touchpoints [claimed create a plurality of contact points, said contact points based on the converted data, the contact points being associated with a user]”);
adjust interactions between the entity and the user based on the contact points (Qualtrics: Pgs. 4-5, Section “What is the return on investment (ROI) on behavioral analytics in business?” discloses “you can tailor your marketing messages [claimed adjust interactions between the entity and the user based on the contact points]”); and
present the adjusted script to an agent using the (Qualtrics: Pg. 9, Section “Customer experience (CX)” discloses “flag words or actions that are off script [claimed adjusted script]” and “customers get the help they expect and agents get valuable time back [claimed present the to an agent using the communication system for an interaction with a user]”).
However, Qualtrics does not explicitly disclose “quantum”, “using one or more quantum computing processors”, “using one or more quantum algorithms”, “convert an audio file into a text file”, “retrieve a series of parameters from the audio file”, “create a plurality of quantum algorithms, the plurality of quantum algorithms for converting the audio file into a text file and a series of parameters”, and “using the quantum communication system”.
Further, Yang is in the same field of endeavor and teaches “quantum”, “using one or more quantum computing processors”, “using one or more quantum algorithms”, “convert an audio file into a text file”, “retrieve a series of parameters from the audio file”, “create a plurality of quantum algorithms, the plurality of quantum algorithms for converting the audio file into a text file and a series of parameters”, and “using the quantum communication system” (Yang: Pg. 1, Section “1. INTRODUCTION” discloses “quantum machine learning (QML) [claimed quantum] becomes an ideal building block for VFL”; Yang: Pg. 1, Section “ABSTRACT” discloses “quantum computing server [claimed using one or more quantum computing processors]”; Yang: Pg. 1, Section “1. INTRODUCTION” discloses “quantum algorithm [claimed using one or more quantum algorithms]” and “random quantum circuit [claimed create a plurality of quantum algorithms, the plurality of quantum algorithms]”; Yang: Pg. 3, Section “4.2. Encoded Acoustic Features from Quantum Device” discloses “audio transcription [claimed convert an audio file into a text file]” and “audio transcription [claimed for converting the audio file into a text file]”; Yang: Pg. 2, Section “3.1. Speech Processing under Vertical Federated Learning” discloses “Mel spectrogram feature vectors are extracted [claimed retrieve a series of parameters from the audio file]” and “Mel spectrogram feature vectors are extracted [claimed and a series of parameters]”; Yang: Pg. 1, Section “1. INTRODUCTION” discloses “decentralized quantum-based ASR system [claimed using the quantum communication system]”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Qualtrics-Yang before him or her, to modify the communication system of Qualtrics to include the quantum computing processors, quantum algorithms, audio conversion, and parameter retrieval as described in Yang. The motivation for doing so would have been to improve the security, feature extraction capabilities, and computational efficiency of the acoustic data processing pipeline (Yang: Pg. 1, Section “1. INTRODUCTION”).
However, Qualtrics-Yang do not explicitly disclose “a single one of the plurality of points corresponding to an up-spin, a single one of the plurality of points corresponding to a down-spin and at least one of the plurality of points corresponding to a superposition of the up-spin and the down-spin”.
Furthermore, Hui is in the same field of endeavor and teaches “a single one of the plurality of points corresponding to an up-spin, a single one of the plurality of points corresponding to a down-spin and at least one of the plurality of points corresponding to a superposition of the up-spin and the down-spin” (Hui: Pg. 8, Section “Unitary” discloses “an up-spin or a down-spin state [claimed a single one of the plurality of points corresponding to an up-spin, a single one of the plurality of points corresponding to a down-spin]” and “change the superpositions to [claimed at least one of the plurality of points corresponding to a superposition of the up-spin and the down-spin]”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Qualtrics-Yang, and Hui before him or her, to modify the quantum contact points of Qualtrics-Yang to include the up-spin, down-spin, and superposition states feature as described in Hui. The motivation for doing so would have been to improve processing efficiency by leveraging the fundamental mechanics of qubits to represent the varying states of the encoded touchpoints, thereby enabling robust representations for quantum processing algorithms.
As per claim 15, Qualtrics-Yang-Hui disclose the system of claim 14 wherein the plurality of data is collected from multiple sources (Qualtrics: Pg. 6, Section “How does behavioral analytics work?” discloses “collected through a behavioral analytics platform, from every user interaction with digital channels [claimed Qualtrics-Yang-Hui disclose the system of claim 14 wherein the plurality of data is collected from multiple sources]”),
the multiple sources including:
private sources (Qualtrics: Pg. 6, Section “How does behavioral analytics work?” discloses “previous transactional records [claimed private sources]”); and
public sources (Qualtrics: Pg. 10, Section “Employee experience (EX)” discloses “external forums, social media interactions, and review sites [claimed public sources]”).
As per claim 16, Qualtrics-Yang-Hui disclose the system of claim 14 wherein the user-relevant historical data includes a plurality of previous interactions (Qualtrics: Pgs. 2-3, Section “What is behavioral analytics?” discloses “past and present activity [claimed the user-relevant historical data includes a plurality of previous interactions]”).
As per claim 17, Qualtrics-Yang-Hui disclose the system of claim 14 wherein the behavioral data includes current interactions with the user (Qualtrics: Pg. 6, Section “How does behavioral analytics work?” discloses “user behavior on a website or product in real time [claimed the behavioral data includes current interactions with the user]”).
As per claim 18, Qualtrics-Yang-Hui disclose the system of claim 14 wherein shrinking the data relevancy-wise includes shrinking the data for a single user (Qualtrics: Pgs. 4-5, Section “What is the return on investment (ROI) on behavioral analytics in business?” discloses “connect with each one personally [claimed shrinking the data for a single user]”).
As per claim 19, Qualtrics-Yang-Hui disclose the system of claim 14 wherein the contact points include: a plurality of hobbies associated with the user (Qualtrics: Pg. 3, Section “When and where do we see behavioral analytics in business?” discloses “analyzing a customer's choices and tastes [claimed a plurality of hobbies associated with the user]”);
a time preferable to conduct the call (Qualtrics: Pg. 10, Section “Employee experience (EX)” discloses “understand how and when work gets done by linking it to productivity systems such as calendars [claimed a time preferable to conduct the call]”); and
a location associated with the user (Qualtrics: Pgs. 2-3, Section “What is behavioral analytics?” discloses “geographic and demographic data [claimed a location associated with the user]”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and can be viewed in the list of references.
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/PEET DHILLON/Primary Examiner
Art Unit: 2488
Date: 08-05-2026
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